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WO2015162158A1 - Suivi de mouvement d'être humain - Google Patents

Suivi de mouvement d'être humain Download PDF

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Publication number
WO2015162158A1
WO2015162158A1 PCT/EP2015/058672 EP2015058672W WO2015162158A1 WO 2015162158 A1 WO2015162158 A1 WO 2015162158A1 EP 2015058672 W EP2015058672 W EP 2015058672W WO 2015162158 A1 WO2015162158 A1 WO 2015162158A1
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Prior art keywords
points
skeletal
subject
marker
motion capture
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English (en)
Inventor
Victor SHOLUKHA
Serge Van Sint Jan
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Universite Libre de Bruxelles ULB
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Universite Libre de Bruxelles ULB
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1126—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique
    • A61B5/1128—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb using a particular sensing technique using image analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1113—Local tracking of patients, e.g. in a hospital or private home
    • A61B5/1114—Tracking parts of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb
    • A61B5/1121—Determining geometric values, e.g. centre of rotation or angular range of movement

Definitions

  • the present invention is in the field of 3-dimensional modelling of skeletal kinematics of a subject.
  • Gaming camera devices such as the Microsoft KinectTM sensor commercially developed by PrimeSense technology (Tel Aviv, Israel), herein simply referred to as Kinect, are markerless motion devices. Such devices provide a cheap and easy to use way to obtain motion information of a person. They typically use a stick model skeleton to model the person.
  • markerless systems depends on the number of cameras used (single camera vs. multiple cameras systems), types of algorithms (such as annealed particle filtering, stochastic propagation, silhouette contour, silhouette based techniques), and whether the entire body is estimated or only a specific region.
  • the skeleton model may comprise 20 points, located only approximately at the joint level, which movements are being captured by a camera. These 20 points are gross estimations of the centre of the major joints of the human body (FIG. 1 ).
  • This kind of model only allows for very simple motion assessment (e.g., vector angle between 3 points for knee or elbow flexion, simple geometric approach to estimate shoulder abduction between upper arm and thorax) with limited precision. This leads to a relatively crude assessment of motion.
  • the field of motion assessment for example in a clinical motion analysis or biomechanics, requires a larger set of skeletal points. Furthermore, the skeletal points need to be anatomically accurate, and need to correspond to actual joints in the body. Clinical conventions using such points are typically based on anatomical planes.
  • the present invention overcomes one or more of the aforementioned disadvantages, based on advanced 3-dimensional modelling of skeletal kinematics of a subject.
  • Preferred embodiments of the present invention overcome one or more of the aforementioned disadvantages.
  • the present invention provides a computer-implemented method for 3-dimensional modelling of skeletal kinematics of a subject, wherein the method comprises a data enrichment phase, wherein the data enrichment phase comprises the steps of:
  • the invention provides a method for 3-dimensional modelling of skeletal kinematics of a subject, comprising the steps of:
  • the invention provides a method according to the first or second aspect as described above, wherein the non-marker based motion capture device in step a) is a feature extraction-based motion capture device, preferably a markerless camera device, more preferably a gaming camera device.
  • the non-marker based motion capture device in step a) is a feature extraction-based motion capture device, preferably a markerless camera device, more preferably a gaming camera device.
  • the invention provides a method according to the first or second aspect as described above, wherein the set of N skeletal points comprises at most 28 points, preferably at most 26 points, preferably at most 24 points, preferably at most 22 points, preferably 20 points. In some preferred embodiments, the invention provides a method according to the first or second aspect as described above, wherein the set of M skeletal points comprises at least 30 points, preferably at least 31 points, preferably at least 32 points, preferably at least 33 points, preferably 34 points.
  • the invention provides a method according to the first or second aspect as described above, wherein step b) is performed by processing each link sequentially from the root, preferably the pelvis, to the end joint.
  • the invention provides a method according to the first or second aspect as described above, wherein external constraints, for example floor constraints, are used in step b), preferably based on a 3-point algorithm.
  • the invention provides a method according to the first or second aspect as described above, wherein additional points in step b) are obtained based on the orientation of two neighbouring links.
  • the invention provides a method according to the first or second aspect as described above, further comprising the step of:
  • the invention provides a method according to the first or second aspect as described above, wherein the set of M skeletal points in step c) corresponds to the standard output of a marker-based motion capture system, for example a marker-based stereophotogrammetry motion capture system.
  • a marker-based motion capture system for example a marker-based stereophotogrammetry motion capture system.
  • the invention provides a method according to the first or second aspect as described above, wherein step c) comprises outputting the set of M skeletal points into a conventional gait model, preferably into a plug-in-gait motion representation model.
  • the invention provides a method according to the first or second aspect as described above, wherein steps a) to c) are performed in real-time.
  • the invention provides a method according to the first or second aspect as described above, wherein the subject is a human subject.
  • external constraints are used based on a 3-point algorithm. This means that a a 3-point algorithm is used when imposing the external constraints.
  • the 3-point algorithm comprises a pose estimation based on the determination of a triangle representing the limb-of-interest from the original linear representation.
  • the present invention provides a computer-implemented method for performing a postural or gait analysis of a subject, comprising the method for modelling skeletal kinematics of the subject according to the first or second aspect of the invention and preferred embodiments thereof.
  • the present invention provides use of the method according to the first or second aspect of the invention and preferred embodiments thereof for biomechanics and/or clinical functional analysis.
  • FIG. 1 Stick model skeleton obtained with the KinectTM sensors and Kinect for
  • FIG. 2 illustrates the anterior view of a stick model diagram of a subject in the upright position, tracked with the KinectTM sensor, showing that the joint centres (numbered according to Kinect for Windows SDK output) are well recognized.
  • FIG. 2B and 2C illustrates the anterior and lateral view respectively of a stick model diagram of the subject performing a deep squat movement, tracked with the KinectTM sensor. The arrow indicates that the left knee is wrongly tracked.
  • FIG. 3 are similar to FIG. 2A-C.
  • FIG. 3D and 3E show the same squat motion for the 347 skeletal points, the arrow indicating that the left knee is in a more natural position.
  • FIG. 3F illustrates the optimized skeleton in the upright position.
  • FIG. 4 illustrates the raw Kinect model (upper body) with 14 skeletal points
  • FIG. 4B illustrates the optimized model with 220 skeletal points
  • FIG. 4C illustrates the optimized model fused with a generic skeleton.
  • FIG. 5 Right knee position correction taking into account floor constraints (right posterolateral view).
  • FIG. 6A-B Example of right knee and ankle joint angles reconstruction from extended 3D model (FIG. 6A) and comparison with original stick-based model (FIG. 6B).
  • FIG. 7 Flow chart illustrating a preferred embodiment of the invention.
  • the present invention concerns a computer-implemented method for 3-dimensional modelling of skeletal kinematics of a subject, wherein the method comprises a data enrichment phase, wherein the data enrichment phase comprises the steps of: a) obtaining a set of N skeletal points from a non-marker-based motion capture device;
  • the invention provides a method for 3-dimensional modelling of skeletal kinematics of a subject, comprising the steps of:
  • the invention provides a method for 3-dimensional modelling of skeletal kinematics of a subject, comprising the steps of:
  • the method according to the first or second aspect of the invention comprises the step of:
  • non-marker-based motion capture device preferably refers to a markerless motion capture device, such as a markerless camera.
  • the non-marker-based motion capture device may comprise a single camera, or multiple cameras.
  • the N skeletal points are obtained from single camera markerless data (e.g. the Kinect).
  • Other 3D camera's that may be used can be commercially provided by Huawei (such as the Xtion Pro Live), Mesa imaging, Intel (such as the Creative Interactive Gesture Camera Developer Kit, or the RealSense 3D Camera), Panasonic (such as the D-imager), Softkinetic (such as DepthSense), and Simi (such as the Simi Shape 3D).
  • Each frame of the original data can be collected from the original hardware and available as 2D or 3D coordinates of crude approximation of the main human joints (e.g., shoulder, elbow, hip, knee, etc.). By piecewise linear connection of those joints one can develop stick-based model (i.e., each adjacent set of point is linked together by a line representing human segments) for visualization and motion analysis.
  • stick-based model i.e., each adjacent set of point is linked together by a line representing human segments
  • Single camera markerless data are preferably captured and recognized in real time.
  • these data usually require filtering (e.g., point trajectories smoothing), reframing (i.e., to guaranty frame frequency stability) and calibration.
  • the non-marker based motion capture device in step a) is a feature extraction-based motion capture device, preferably a markerless camera device, more preferably a gaming camera device (for example a Microsoft KinectTM device).
  • the set of N skeletal points was obtained by feature extraction of N joints.
  • the set of N skeletal points is insufficient for full 3- dimensional skeletal kinematics analysis of the subject, but wherein the set of M skeletal points allows for full 3-dimensional skeletal kinematics analysis of the subject.
  • the set of N skeletal points may only provide a simplified representation.
  • the set of N skeletal points comprises at least 16 points and at most 28 points, preferably at least 17 points and at most 26 points, preferably at least 18 points and at most 24 points, preferably at least 19 points and at most 22 points, preferably 20 points.
  • the set of N skeletal points comprises at most 28 points, preferably at most 26 points, preferably at most 24 points, preferably at most 22 points, preferably 20 points.
  • the set of N skeletal points may comprise 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, or 28 points.
  • only a subset of the set of N skeletal points is used in the present method, comprising fewer points that the set of N skeletal points as set out above.
  • the method according to the first or second aspect of the invention comprises the step of:
  • constraints are a constant limb length for one or more limbs, preferably for all limbs (i.e., lower and upper limbs, and trunk).
  • the limb length is obtained from a starting pose, preferably a vertical upright static starting pose.
  • Other preferred constraints can be a floor constraint, whereby the vertical position of a skeletal point of a lower limb, ankle, or foot is constrained by the vertical position of the starting pose, i.e. by the floor.
  • points (M>N) is not an optimisation step as such. It is a step used to obtain a set of initial conditions, that may be used as a starting point for an additional optimisation step.
  • the inventors have surprisingly found that the initial conditions obtained by the method of the invention are of such high quality, that a further optimisation step may not be necessary, but merely optional.
  • the initial conditions obtained by the method of the invention provide an improved starting point for such an optimisation step.
  • a calibration procedure is adopted that is preferably based on static pose capturing. Scaling factors are estimated by comparison with external anthropometric body links measurements (for example, directly measured by the therapist in charge of the motion data capture).
  • the link size is corrected based on the assumption that the set of N skeletal points (for example, a raw stick-based model) supplies proper line orientation.
  • the method starts from the native pelvis stick model. This has the advantage that it is usually the most reliable and stable data collected from a markerless camera.
  • step b) is performed by processing each link sequentially from the root, preferably the pelvis, to the end joint.
  • FIG.5 shows an example of such an approach (for the right leg, HC- ⁇ - ⁇ obtained from raw data HCA).
  • Constant limb length implementation may be based on analysis of the current frame joint 3D positions:
  • Joint connection may be defined by a topology table:
  • a normalized orientation vector may be calculated as:
  • external constraints for example external floor constraints
  • step b external constraints
  • the constraints are applied based on a three-point algorithm for the pose estimation.
  • the estimation is preferably based on the determination of a triangle representing the limb-of-interest from the original linear representation. Such estimation requires therefore the determination of a supplementary point, or vertex, in order to define the entire segment triangle. This is clarified below by taking an example of a foot on the floor, taken as external constraint, during squat motion, and as illustrated in FIG. 5 and FIG. 6, by keeping the lower limb length constant.
  • FIG. 5 illustrates a right knee position correction according to a preferred embodiment of the invention, taking into account floor constraints (right posterolateral view).
  • Points A, H, and C are points estimated from a markerless camera.
  • Point K is a reconstructed new knee position taking into account constant size of the links.
  • Point P is the projection of K on the HA side.
  • Small balls near knee (K) and ankle (A) are newly-estimated points determined by the invention and available from the extended model; these points give an estimation of the frontal plane (for the knee, defined by medial and lateral epicondyle landmarks, for the ankle joint, defined by the medial and lateral malleolus landmarks).
  • the skeleton HdA-i shows the right lower limb size estimated without external constraints, note the exaggerated lower limb length compared to the real skeleton.
  • the skeleton HKA shows the same lower limb corrected with the external floor constraints. Without loss of generality, it may be assumed that the total leg length is elongated due to thigh and shank link sizes correction in order to keep a constant lower limb length.
  • the new knee joint position can then be estimated by a square calculation of the AKH triangle with side lengths from the distance between hip and ankle and new thigh and shank sizes (e.g., using well-known available algorithms such as the Heron's formula). Then the projection (point P) of the new knee joint point towards the hip-ankle line can be estimated from the previously- determined AKH triangle. Note that the distance between hip and ankle joints, and the estimation of the new knee position lay in the same plane as the original one.
  • the last supposition does not allow satisfactory solution when the current knee angle is small (e.g., when lies within the expected error of measurement).
  • the normal illustrated by the lateral arrow on the pelvis local coordinate system visible in FIG. 5
  • the pelvis frontal plane and thigh (or shank) can be used instead for the new knee position evaluation.
  • the thigh link defined between the hip joint point and the new knee joint point can be supplied by two additional points within the frontal plane (e.g., small balls in FIG. 5) using thigh and shank orientations.
  • additional points in step b) are obtained based on the orientation of two neighbouring links. This approach is preferably used for the lower limbs (knee and ankle joints) and the upper limb (elbow joint) extremities. It can be extended straightforwardly for any other joints if the original data comprise enough information about the linear segment architecture of the body segments to analyse.
  • step b) When additional points in step b) are obtained based on the orientation of two neighbouring links, this allows for a more accurate representation of certain limbs, which is more realistic from an anatomical viewpoint.
  • two additional points may be obtained perpendicular to the plane comprising two neighbouring links, for example for a lower limb. Without these additional points, only the flexion/extension of the lower limb can be accurately estimated, but with the addition of these points, the 3D rotation of the thigh may be estimated as well.
  • this When applied to the elbow joint, this may also allow for the 3D movement of the shoulder (e.g. humerus).
  • the wrist joint this may also allow for the 3D movement of the forearm (e.g. ulna and radius).
  • anatomical knowledge is additionally used to create new skeletal points. For example, some angles, or some directions of motions are anatomically impossible or improbable.
  • the anatomical knowledge allows for additional constraints to be imposed on the motion of the skeletal points, from which new skeletal points may be obtained.
  • the method according to the first or second aspect of the invention (and preferred embodiments thereof) further comprises the step of:
  • FIG. 6 presents an example of results of lower limbs performing deep squatting.
  • Original raw data from markerless cameras are preferably first filtered and pre-processed. From these data, relative angular orientation of the segments can be estimated. In addition, the same data are processed by the method as described above and supplementary points are estimated. From all available points (raw and estimated), anatomical frames can be obtained (here for the pelvis, thigh and shank with the axes X, Y, and Z respecting clinical conventions). From the anatomical frames, segment orientation can be processed.
  • Kinect data (20 points per frame) and converted to PiG like (PiG refers to "Plug in Gait" which is used in the Vicon Clinical Manager software package) data (34 points per frame).
  • results show acceptable agreement in the sagittal plane, i.e., around the Z-axis (right laterally oriented) in FIG. 6A, between the stick model and PiG model: here in this particular example, the maximal difference for the knee joint is about 8°, as shown in FIG. 6B.
  • Values of flexion angles for the stick model (N skeletal points) are 60° and 92° for the hip and knee joint, respectively.
  • Flexion angles values estimated from the extended model (M skeletal points) are 96°(knee) and 67°(ankle). Supplementary angles amplitude values are in the range [0,20]°, which is within normal anatomical range. Note that in FIG.
  • the method according to the first or second aspect of the invention comprises the step of: c) outputting the set of M skeletal points into a six d eg rees-of -freedom joint skeletal kinematics analysis system.
  • the M points may be used to determine six degrees-of-freedom joint kinematic (i.e., angle, translation, velocity, and acceleration) directly without optimisation.
  • Results are preferably further presented according to conventional clinical and anatomical motion representations. Representing six degrees-of-freedom joint kinematic is an important issue for further realistic representations of joint behaviour and joint kinematics. Such representation allows production of quantitative results that answer and respect current biomechanical and clinical standards (which is impossible when using the linear output of current markerless cameras).
  • a six degrees-of-freedom joint description also improves joint kinematic representation for further modelling (e.g., processing muscle and ligament behaviour during musculoskeletal modelling based on the six degrees-of-freedom produced by the invention).
  • hip, knee and ankle joints crossed by so-called double joint muscles (e.g. quadriceps etc) and their moment arm evaluation is very sensitive to the amount of joint degrees of freedom, especially when knee and ankle joint translations are compared to simplified rotational degrees of freedom only.
  • the set of M skeletal points comprises at least 30 points and at most 42 points, preferably at least 31 points and at most 40 points, preferably at least 32 points and at most 38 points, preferably at least 33 points and at most 36 points, preferably 34 points.
  • the set of M skeletal points comprises at least 30 points, preferably at least 31 points, preferably at least 32 points, preferably at least 33 points, preferably 34 points.
  • the set of M skeletal points may comprise 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, or 40 points.
  • the set of M skeletal points comprises at least 40 points, preferably at least 50 points, preferably at least 75 points, preferably at least 125 points, preferably at least 150 points, preferably at least 175 points, preferably at least 200 points.
  • the new model architecture allows for proper anatomical and clinical motion representations, and further analysis for example to be included in a conventional patient file.
  • the final enriched model is preferably similar to current standards in clinical motion analysis (such as the PiG model used by the VICON company, or any clinical conventions recommended by standardization committees, such as for example the International Society of Biomechanics, the Gait and Clinical Movement Analysis Society, the European Society for Movement Analysis in Adults and Children, etc.)-
  • the set of M skeletal points corresponds to the standard output of a marker- based motion capture system, for example a marker-based stereophotogrammetry motion capture system (such as most available marker-based systems found on the market, e.g. VICON, MotionAnalysis, etc).
  • step c) comprises outputting the set of M skeletal points into a conventional gait model, preferably into a plug-in-gait motion representation model.
  • steps a) to c) are performed in real-time.
  • the subject can be a human, animal or robotic subject, preferably a human or animal subject. In some preferred embodiments, the subject is a human subject.
  • the present invention provides a computer-implemented method for performing a postural or gait analysis of a subject, preferably according to world-wide clinical conventions, comprising the method for modelling skeletal kinematics of the subject according to the first or second aspect of the invention and preferred embodiments thereof.
  • the present invention provides use of the method according to the first, second, or third aspect of the invention and preferred embodiments thereof for biomechanics and/or clinical functional analysis, preferably related to the assessment, diagnosis or follow-up of the musculoskeletal systems in normal subjects (e.g., body tracking, sport analysis, etc) or in patients (e.g., suffering from musculoskeletal or neurological disorders leading to locomotor disturbances).
  • biomechanics and/or clinical functional analysis preferably related to the assessment, diagnosis or follow-up of the musculoskeletal systems in normal subjects (e.g., body tracking, sport analysis, etc) or in patients (e.g., suffering from musculoskeletal or neurological disorders leading to locomotor disturbances).
  • the present invention provides a computer program, or a computer program product directly loadable into the internal memory of a computer, or a computer program product stored on a computer readable medium, or a combination of such computer programs or computer program products, for performing the method according to the first, second, or third aspect of the invention and preferred embodiments thereof.
  • the invention also provides a computer or a computer- readable medium on which the computer program or computer program product according to the fifth aspect is stored.
  • a skeletal kinematics analysis system is also stored on the computer or computer-readable medium. Preferred embodiments for the skeletal kinematics analysis system are as described above for the first or second aspect of the invention.
  • the invention also provides a system suitable for performing the method according to the first, second, or third aspect of the invention. According to a seventh aspect, the invention also provides a system, comprising:
  • non-marker-based motion capture device preferably a feature extraction-based motion capture device, more preferably a markerless camera device, more preferably a gaming camera device;
  • the system according to the seventh aspect of the invention also comprises a skeletal kinematics analysis system.
  • Preferred embodiments for the skeletal kinematics analysis system are as described above for the first or second aspect of the invention.
  • FIG. 7 shows a flow chart illustrating a preferred embodiment of the method of the invention using a system of the invention.
  • a non-marker based motion capture device captures images of a subject and provides N skeletal points of the subject for each image. Using constant limb length of at least one limb, the data enrichment phase converts these N skeletal points into M skeletal points. The M skeletal points are subsequently used as input for a 6 degrees of freedom (6DoF) skeletal kinematics system to provide a more detailed image. Based on the input of M skeletal points, the 6DoF skeletal kinematics system can be used for analysis of the subject, for example postural or gait analysis.
  • 6DoF 6 degrees of freedom
  • the MLS (KinectTM sensor, frequency: 30 Hz) was used as a motion analysis device.
  • Custom-made software integrating the native Microsoft Kinect SDK (version 1.6) was developed to collect the raw MLS data (Fig.lA). Note that these data were relatively crude (i.e., stick-like model without any real 3D representation of anatomical segments).
  • the MLS sensor was placed on a tripod at 1.5 m above the floor. Subjects stood at 2.5 m from the camera. Subjects were in underwear to allow reliable placement of the markers for the MBS analysis taking place simultaneously. Prior to motion analysis, the subjects were asked to stand still in anatomical position facing the MLS camera. Subjects were then asked to maintain three different poses (3s for each of the poses) before recording the motion in order to calibrate the MLS data processing pipeline.
  • MBS data were simultaneously collected from a state-of-the-art stereophotogrammetric system (Vicon, 8 MXT40s cameras, Vicon Nexus software, frequency: 90Hz) that tracks the spatial trajectories of the reflective markers set on the subjects.
  • a modified PiG model has been adopted.
  • markers have been set on the medial epicondyle of the humeral and femoral bones. Thirty four markers were positioned by the same observer during the entire study.
  • PiG like model using a method according to the present invention: Following the method that introduced a model based approach for interpolating additional markers, the 20 anatomical landmarks (ALs) from the raw MLS model were converted to 34 ALs to obtained a "PiG-like" model. Then, for each limb-of-interest an anatomical local coordinate system (LCS) was created based on three ALs. For pelvis and thorax four ALs were used for the LCS definition. From these LCS, joint anatomical angles were computed using Euler's angles.
  • ALs anatomical landmarks
  • LCS anatomical local coordinate system

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Abstract

L'invention concerne un procédé mis en œuvre par ordinateur permettant la modélisation tridimensionnelle de cinématique squelettique d'un sujet, le procédé comprenant une phase d'enrichissement de données, la phase d'enrichissement de données comprenant les étapes consistant : a) à obtenir un ensemble de N points squelettiques à partir d'un dispositif de capture de mouvement non basé sur un marqueur ; b) à convertir l'ensemble de N points squelettiques en un ensemble de M points squelettiques, où M > N, par étalonnage d'une longueur de membre constante d'au moins un membre ; et c) à délivrer l'ensemble de M points squelettiques dans un système d'analyse de cinématique squelettique d'articulation à six degrés de liberté.
PCT/EP2015/058672 2014-04-22 2015-04-22 Suivi de mouvement d'être humain Ceased WO2015162158A1 (fr)

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CN106346485A (zh) * 2016-09-21 2017-01-25 大连理工大学 基于人手运动姿态学习的仿生机械手的非接触式控制方法
CN107274466A (zh) * 2017-05-31 2017-10-20 珠海金山网络游戏科技有限公司 一种实时全身动作捕捉的方法、装置和系统
CN107272593A (zh) * 2017-05-23 2017-10-20 陕西科技大学 一种基于Kinect的机器人体感编程方法
CN107274464A (zh) * 2017-05-31 2017-10-20 珠海金山网络游戏科技有限公司 一种实时交互3d动画的方法、装置和系统
CN107349570A (zh) * 2017-06-02 2017-11-17 南京邮电大学 基于Kinect的上肢康复训练与评估方法
CN107469294A (zh) * 2016-06-08 2017-12-15 上海体育学院 一种基于kinect硬件的乒乓球运动员用动作分析系统
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